How ChatGPT Works: The Model Behind The Bot
![how does ml 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)
Though it’s arguably the most popular AI tool, thanks to its widespread accessibility, OpenAI made significant waves in artificial intelligence by creating GPTs 1, 2, and 3 before releasing ChatGPT. Artificial superintelligence (ASI) would be a machine intelligence that surpasses all forms of human intelligence and outperforms humans in every function. A system like this wouldn’t just rock humankind to its core — it could also destroy it. If that sounds like something straight out of a science fiction novel, it’s because it kind of is. Artificial narrow intelligence (ANI) refers to intelligent systems designed or trained to carry out specific tasks or solve particular problems without being explicitly designed.
- The Connectivity Standards Alliance has finalized the Matter 1.4 spec, releasing it to accessory makers and platforms like Apple Home with several new device types and improvements.
- This indicates that a well-balanced and holistic approach to technological advancement and ethics will be required to maximize the benefits of AI while mitigating its risks.
- That video took a team of editors working for a TV program, but now we’re looking at a world where that can be done in minutes by anyone with access to a mid-tier gaming computer.
- Engineers in the field of artificial intelligence must balance the needs of several stakeholders with the need to do research, organize and plan projects, create software, and thoroughly test it.
- The pre-processing required in a ConvNet is much lower as compared to other classification algorithms.
However, on average, it may take around 6 to 12 months to gain the necessary skills and knowledge to become an AI engineer. This can vary depending on the intensity of the learning program and the amount of time you devote to it. Engineers in the field of artificial intelligence must balance the needs of several stakeholders with the need to do research, organize and plan projects, create software, and thoroughly test it. The ability to effectively manage one’s time is essential to becoming a productive member of the team.
What is Data Augmentation in Deep Learning?
They’re responsible for designing, modeling, and analyzing complex data to identify business and market trends. AI engineers work with large volumes of data, which could be streaming or real-time production-level data in terabytes or petabytes. For such data, these engineers need to know about Spark and other big data technologies to make sense of it. Along with Apache Spark, one can also use other big data technologies, such as Hadoop, Cassandra, and MongoDB. AI Engineers build different AI applications, such as contextual advertising based on sentiment analysis, visual identification or perception and language translation.
![how does ml 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)
The intermediate challenge is ensuring the system can operate effectively in various environmental conditions and accurately distinguish between normal and anomalous activities. A traffic prediction and management system uses AI to analyze traffic data in real time and predict traffic conditions, helping to manage congestion and optimize traffic flow. AI benefits manufacturing through predictive maintenance, ChatGPT App optimized production processes, and enhanced supply chain management. Transportation has seen improved safety and efficiency with autonomous vehicles and intelligent traffic management systems. Personalized learning experiences created by AI make education more accessible and tailored to individual needs. Learn more about how deep learning compares to machine learning and other forms of AI.
Top Deep Learning Interview Questions and Answers for 2024
With a structured learning approach and industry-relevant projects, you will be able to tackle complex challenges and stay at the forefront of this field. The first ANE that debuted within Apple’s A11 chip in 2017’s iPhone X was powerful enough to support Face ID and Animoji. By comparison, the latest ANE in the A15 Bionic chip is 26 times faster than the first version. Nowadays, ANE enables features like offline Siri, and developers can use it to run previously trained ML models, freeing up the CPU and GPU to focus on tasks that are better suited to them. ANE makes possible advanced on-device features such as natural language processing and image analysis without tapping into the cloud or using excessive power. Of course, there are other data processing stuff which we need to do based on the different type of algorithms used, but we at least have a categorical target now to classify on.
![how does ml 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)
As machine learning evolves, the importance of explainable, transparent models will only grow, particularly in industries with heavy compliance burdens, such as banking and insurance. Clean and label the data, including replacing incorrect or missing data, reducing noise and removing ambiguity. This stage can also include enhancing and augmenting data and anonymizing personal data, depending on the data set.
Market Research
These advancements are bringing us closer to autonomous driving by enhancing current vehicle safety systems. Another significant disadvantage is the high computational power required to train and deploy CNNs effectively. Advanced hardware, such as GPUs, is often necessary, which increases costs and limits access for those without these resources. This makes it difficult for smaller organizations to utilize CNNs efficiently.
![how does ml 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)
In the unified set of APIs that Vertex AI provides, you can treat all these models in the same way. Some of the features mentioned above, like image recognition, also function without an ANE present but will run much slower and tax your device’s battery. One method to reduce ChatGPT replications is to apply a process called full parameter sharding, where only a subset of the model parameters, gradients, and optimizers needed for a local computation is made available. An implementation of this method, ZeRO-3, has already been popularized by Microsoft.
Python is simple and readable, making it easy for coding newcomers or developers familiar with other languages to pick up. Python also boasts a wide range of data science and ML libraries and frameworks, including TensorFlow, PyTorch, Keras, scikit-learn, pandas and NumPy. Similarly, standardized workflows and automation of repetitive tasks reduce the time and effort involved in moving models from development to production. After deploying, continuous monitoring and logging ensure that models are always updated with the latest data and performing optimally.
![how does ml 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)
If an organization can accommodate both needs, deep learning can be used in areas such as digital assistants, fraud detection and facial recognition. Deep learning also has a high recognition accuracy, which is crucial for other potential applications where safety is a major factor, such as in autonomous cars or medical devices. Artificial Intelligence is the process of building intelligent machines from vast volumes of data. Systems learn from past learning and experiences and perform human-like tasks. AI uses complex algorithms and methods to build machines that can make decisions on their own.
There’s also ongoing work to optimize the overall size and training time required for LLMs, including development of Meta’s Llama model. Llama 2, which was released in July 2023, has less than half the parameters than GPT-3 has and a fraction of the number GPT-4 contains, though its backers claim it can be more accurate. LLMs will continue to be trained on ever larger sets of data, and that data will increasingly be better filtered for accuracy and potential bias, partly through the addition of fact-checking capabilities. It’s also likely that LLMs of the future will do a better job than the current generation when it comes to providing attribution and better explanations for how a given result was generated. Machine learning engineers and data scientists work closely with each other and both require sufficient data management skills.
Free Baby Monitoring System with ML for Safe Activity Transmission – Netguru
Free Baby Monitoring System with ML for Safe Activity Transmission.
Posted: Thu, 08 Feb 2024 08:00:00 GMT [source]
Known as market segments, these customer groups can improve marketing efforts. There are many types of clustering algorithms, but K-means and hierarchical clustering are the most widely available in data science tools. Developing an Intelligent Video Surveillance System involves using AI to analyze video feeds in real-time for security and monitoring purposes. This project requires the application of computer vision techniques to detect movements, recognize faces, and identify suspicious behaviors.
A machine learning engineer is a person in IT who focuses on researching, building and designing self-running artificial intelligence systems to automate predictive models. ML engineers design and create AI algorithms capable of learning and making predictions that define machine learning. Developing a Conversational AI for Customer Service involves creating intelligent chatbots and virtual assistants capable of handling customer queries with human-like responsiveness. This intermediate project focuses on natural language processing (NLP) and machine learning to process and understand customer requests, manage conversations, and provide accurate responses.
Additionally, with new chapters opening up every year, the initial research into AI’s leap into the unknown has evolved into more of a leap of faith. Technologies like ChatGPT and “AI art” or whatever are not in any meaningful way “artificial how does ml work intelligence”. They just apply algorithms to massive amounts of data to spit out something that looks real, but still falls straight into the uncanny valley, and these technologies have no sense of awareness of what they are doing.
![how does ml work](data:image/jpeg;base64,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)
The activation layer introduces nonlinearity into the network by applying an activation function to the output of the previous layer. Common activation functions, such as ReLU, Tanh, and Leaky ReLU, transform the input while keeping the output size unchanged. A Smart Agriculture System integrates AI with IoT devices to monitor crop health, predict yields, and optimize farming practices. This intermediate project requires the development of models that can analyze data from soil sensors, drones, and weather forecasts to make decisions about irrigation, fertilization, and pest control.
How to Become a Machine Learning Engineer in 2024? Roadmap – Simplilearn
How to Become a Machine Learning Engineer in 2024? Roadmap.
Posted: Wed, 04 Sep 2024 07:00:00 GMT [source]
This is the first step in the process of extracting valuable features from an image. A convolution layer has several filters that perform the convolution operation. For business applications, clustering is a battle-tested tool in market segmentation and fraud detection. Clustering is also useful for categorizing documents, making product recommendations and in other applications where grouping entities makes sense. Pulkit Jain is a Product Manager for Salesforce & Payments at Simplilearn, where he drives impactful product launches and updates.
- It has also developed programs to diagnose eye diseases as effectively as top doctors.
- Conventionally, the first ConvLayer is responsible for capturing the Low-Level features such as edges, color, gradient orientation, etc.
- But the machine learning engines driving them have grown significantly, increasing their usefulness and popularity.
- The next step for some LLMs is training and fine-tuning with a form of self-supervised learning.
Except for the input layer, each node in the other layers uses a nonlinear activation function. This means the input layers, the data coming in, and the activation function is based upon all nodes and weights being added together, producing the output. MLP uses a supervised learning method called “backpropagation.” In backpropagation, the neural network calculates the error with the help of cost function. It propagates this error backward from where it came (adjusts the weights to train the model more accurately). A Real-Time Sports Analytics System uses AI to analyze sports broadcasts and provide live statistics, player performance metrics, and game insights. You can foun additiona information about ai customer service and artificial intelligence and NLP. This intermediate project entails applying computer vision and machine learning algorithms to process video feeds, identify players and actions, and generate predictive analytics.